Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Remarkable effectiveness of the channel or spatial attention mechanisms for producing
more discernible feature representation are illustrated in various computer vision tasks …

Light-weight convolutional neural networks (CNNs) are specially designed for applications
on mobile devices with faster inference speed. The convolutional operation can only capture …

Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

Vehicle detection technology is of great significance for realizing automatic monitoring and
AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of …

Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Since the introduction of Vision Transformers, the landscape of many computer vision tasks
(eg, semantic segmentation), which has been overwhelmingly dominated by CNNs, recently …

Vision transformers (ViTs) have been successfully applied in image classification tasks
recently. In this paper, we show that, unlike convolution neural networks (CNNs) that can be …